Rational Marketing

The Aberdeen Group published a report in December, 2009 that explored customer database practices to reveal how organizations are capturing, storing, analyzing and acting on customer data.

The Best Performing Organizations:

• Currently achieve 163% mean class Return on Marketing Investment (ROMI); 9% average year over year growth in ROMI
• 51% year over year mean class growth in revenue

The best performing firms shared several common characteristics, including:

• 46% access a full view of customers across all departments and functions in the organization (versus 14% among laggards)
• 52% improve or enhance customer data through regular marketing and IT collaboration (versus 21% of laggards)

The study recommended that companies wishing to become more like a best performing organization:

• Develop a formal data hygiene (cleansing, enrichment, de-duplication and regular updates) strategy. A lack of formal data hygiene can prevent organizations from using customer data in more personalized engagement.
• Enhance existing records with periodic augmentation and enrichment. Only 32% of all respondents actively augmented customer data for accuracy. But, 48% plan to incorporate database enrichment services in their 2010 budget for improving the customer database.

What can you do to maintain and enrich your customer information?

1. Even if you don’t want to send mail to your customers right now, process your list through the National Change of Address database. This is a simple service that will provide move information for individuals, families, and businesses. Learn more about your current and previous customers by obtaining their current addresses. Our reports will identify undeliverable and incomplete addresses. You will know that some of your valuable customers have had changes in their lives and organizations.
2. Think about enriching your customer list with more information. For consumers we can add income, demographic and home characteristic information to your existing data file. For businesses we can add “firmographic” data including number of years in business, number of employees, industry classification and estimated annual revenue.

If you are not sure what information would be best for your business, we are great at asking the right questions to get you started.

Direct Marketing Magazine shared some ideas about predictive analytics.

Predictive Analytics Road Map

To get the most out of customer segmentation analysis, organizations could create road maps incorporating the following steps:

1. Determine the Overall Business Objective. Get everyone on the same path and in agreement with what you want to accomplish, such as improving the yield on lead-generation efforts, identifying cross-sell opportunities or identifying customers most likely to go to a competitor.
2. Capture All Potential Customer Data. Segmentation begins with gathering customer data from a wide variety of resources, including data warehouses, point-of-sale systems and loyalty programs. A database of static customer information is valuable, but until key active knowledge is applied—like preferences or motivations—there’s an incomplete picture of the customer. Capturing feedback from any touchpoint—in any language—provides a clearer understanding of customers’ needs, preferences and attitudes, and improves the segmentation process.
3. Perform Recency, Frequency and Monetary (RFM) Analysis. To obtain the most accurate picture of customer lifetime value, organizations first should perform RFM analysis to classify customers according to: those who have spent the most—the most often and most recently; those who have spent the most—the most monetarily, but may not have purchased in a long time; those who spend the most in the fewest number of transactions; and those who spend the least, or rarely, and have not purchased in a long time.
4. Outline the Segmentation Process. Once customers have been identified based on purchasing patterns, then segmentation analysis can begin to get to the core of the audience you want to target. The key to a successful segmentation program is to first define the many ways the results can be used. An approach might take the following path:
   • Create customer segments to enable differential marketing programs.
   • Use past purchase data and demographics to construct customer subgroups.
   • Isolate key performance factors linked to long-term customer value as major data drivers for the segmentation.
   • Use cluster analysis to form homogenous groups of differently valued customers.
   • Use techniques such as rule induction to automatically extract the profile of each cluster.
   • Align the marketing spending priorities against each subgroup.
   • Link product line or category affinity to each subgroup.
   • Develop marketing plans incorporating value-based budgeting and category affinity to make programs more relevant and efficient.
5. Auto-Segmentation. With a customer base more clearly defined through effective segmentation, organizations then can add predictive modeling functionality within each segment to produce greater insight that’s required to more effectively and efficiently acquire, grow and retain the right customers, and also identify fraud and minimize risk. The modeling functionality in predictive analytics technology helps organizations accurately determine which customers best match specific offers or campaigns. By eliminating the guesswork when targeting customer groups, organizations quickly increase ROI through more efficient use of resources and reduced spending.
6. Deploy and Share Results Throughout the Business. The final step is to create an environment in which an organization can manage and automate its analytical processes and easily deploy the results across the enterprise—thus improving productivity and collaboration and increasing ROI. This includes the ability to automate the database scoring process, publish and distribute output and reports, and integrate the analytical process into other business applications. For example, when a customer calls a call center, that agent should be able to pull up information on that specific customer and know what type of offer should be made at that particular time.

Hit the Target

With predictive analytics technology, organizations can move toward a one-to-one conversation with customers. Insight gained from even the most elementary analysis of customer characteristics can have profound implications on the business and result in marketing success.

Direct Marketing Magazine shared some ideas about predictive analytics.

Personalize Customer Relationships

Segmentation is a way of grouping people or organizations with similar demographic profiles, attitudes, purchasing patterns, buying behaviors or other attributes. This helps to understand customers more thoroughly and thus market to them more effectively.

Many businesses use segmentation to recognize that customers have some unique characteristics. But they stop when going further may be possible, for this reason, segmentation can be a “blunt instrument,” leading to “one-to-some” marketing. It can perpetuate “accepted wisdom” about customers and the market that are not necessarily accurate.

Marketers can add predictive analytics to the segmentation process to generate insight needed to more effectively and efficiently acquire, grow and retain the right customers. The result could be a better understanding of what products and services customers are likely to want next. Predictive analytics can be thought of as auto-segmentation. This technology can discover groupings in customer data and find relevant patterns that are likely to be more subtle, extracting greater predictive insight than traditional segmentation. This would ensure that insight obtained into what customers want and how they behave, and marketing decisions made would be evidence-based and result in more profitable outcomes from one-to-one customer interactions.

Predictive analytics incorporates data collection, statistics, modeling and deployment capabilities. This drives the entire segmentation process, from gathering customer information at every interaction to analyzing the data and providing specific, real-time recommendations on the best action to take at a particular time, with a particular customer. The result is more effective customer relationship management strategies, including advertising and marketing campaigns; upsell and cross-sell initiatives; and long-term customer loyalty, retention and rewards programs.

In the next post we will look at a predictive analytics “road map”.

This is the second part from Target Marketing Magazine’s invitation to take a look at who your customers really are.

What Customers Believe

Understanding customer beliefs leads to building a powerful brand and can pay dividends in creative presentations and communications. Psychographics, or attitudinal data, are generally established by evaluating the thoughts and feelings of various cohort groups—groups of consumers in similar age ranges or stages of life.

Overlaying your customer data with psychographic information allows you to classify customers in one of a number of predefined segments. These segments are named and grouped based on common beliefs held among members. Psychographic data goes beyond demographics by exploring how people feel about everything from finances to shopping to technology to family and friends to religion, and more. Often, “messaging briefs” or tips for communicating with each group better and more specifically to their own beliefs also accompany these overlays.

If you conduct demographic and psychographic overlays you produce a picture of what the customer looks like and what she believes. To continue building a better picture of the customer, though, we have to know what she says.

What Customers Will Tell You

A good survey provides actionable answers to questions that discern the respondents in one group from those in another. By building a customer survey—one that can be executed online as well as over the phone—you can get the customer’s opinions of your brand as well as your competitors’ brands. The essence of brand differentiation is defining what you stand for and knowing what you don’t stand for in the mind of the customer.

Asking customers a variety of questions about themselves, your company and the competition, you begin to develop an understanding of what’s important to them in making purchasing decisions and how you rate on those issues versus the competition. In a perfect scenario, you find that you excel in the areas that are most important to your customers and that you have a significant advantage over the competition in those same areas.

Surveys don’t have to be long; seven minutes is an eternity on the phone. What surveys must do is ask the right questions. And to highlight your strengths even more, put non-buyers and lapsed buyers into the survey mix along with “better” customers. This way you see how your best buyers perceive you compared to your older buyers and nonbuyers.

What Customers Buy

Square-inch analysis, or squinch analysis, sheds light on product performance. In an effort to understand customer behavior completely, you should enhance your profiles with merchandise analysis. Asking questions like, “Do my best customers buy differently from my worst customers?” helps you address merchandise mix; price point; and creative issues in the catalog, on the Web site and in direct mail. It is at this stage that you’ve not only developed but implemented a better, more meaningful view of your customers.

Demographics are great for modeling, but if you want to paint the best picture possible of who your customers are, you can  move beyond age and gender to an understanding of what they believe and what they think about your company. Putting all of the pieces of the customer information puzzle together allows you to build more targeted and relevant creative, a more appealing merchandise mix and a more profitable contact strategy by contributing to an ironclad brand positioning. To define yourself, you first must define your customers.

Target Marketing Magazine printed an invitation to take a look at who your customers really are.

Many marketers think of customers in straight-forward terms: females, 45 to 60 years old, $75,000-plus household incomes, for example. These broad-sweeping descriptors have a place in customer definitions but aren’t the end in defining who does business with you.

Many of the data points necessary to understand your customer are available in your database. Purchasing data, for example, provides the foundation of analysis in marketing, merchandising and price points. But some of the more meaningful data—the information that allows you to complete the puzzle—is often available through knowledgeable providers. These professionals can offer data appends that allow you a more complete and robust view of who the customer really is.

Let’s look at several techniques you can use to learn more about your customers (and even your noncustomers) and how, put together, they give you the intelligence and insights to tighten your brand, improve your marketing and boost profits.

What Customers Are

The first step in building a comprehensive customer profile is the application of demographic data. There are more than a thousand different demographic variables that can be appended to the typical consumer’s name and address record. These demographics, or descriptive characteristics, can range from age and income to gender and ethnicity to home ownership and consumer credit availability. By compiling data from a variety of outside sources, a professional list provider can overlay demographics data onto any consumer data file and provide back either a series of descriptive reports or, better yet, appended data for additional analysis.

The demographics most commonly employed in developing customer profiles are generally age, income and gender. Additionally though, it can be powerful to know more about the customer, like how much she paid for her home and how long she’s lived there; how wealthy she is, beyond just annual income estimates; etc. By applying these data points, you start to paint a picture of the person “materially”—essentially the 45- to 60-year-old, $75,000-plus household income female mentioned earlier. The additional demos also allow for more understanding about the kind of home she lives in, how thin she spreads her income, how settled she is and more. And from a marketing standpoint, demographics can enhance RFM (recency, frequency, monetary value) selection as well.

In the next post we will look at customer beliefs (psychographics) and the meaning of purchasing patterns.